Characterization of functional methylomes by next-generation capture sequencing identifies novel disease-associated variants

Fiona Allum, Xiaojian Shao, Frédéric Guénard, Marie Michelle Simon, Stephan Busche, Maxime Caron, John Lambourne, Julie Lessard, Karolina Tandre, Åsa K. Hedman, Tony Kwan, Bing Ge, Lars Rönnblom, Mark I. McCarthy, Panos Deloukas, Todd Richmond, Daniel Burgess, Timothy D. Spector, André Tchernof, Simon MarceauMark Lathrop, Marie Claude Vohl, Tomi Pastinen, Elin Grundberg*, Kourosh R. Ahmadi, Chrysanthi Ainali, Amy Barrett, Veronique Bataille, Jordana T. Bell, Alfonso Buil, Emmanouil T. Dermitzakis, Antigone S. Dimas, Richard Durbin, Daniel Glass, Neelam Hassanali, Catherine Ingle, David Knowles, Maria Krestyaninova, Cecilia M. Lindgren, Christopher E. Lowe, Eshwar Meduri, Paola Di Meglio, Josine L. Min, Stephen B. Montgomery, Frank O. Nestle, Alexandra C. Nica, James Nisbet, Stephen O'Rahilly, Leopold Parts, Simon Potter, Johanna Sandling, Magdalena Sekowska, So Youn Shin, Kerrin S. Small, Nicole Soranzo, Gabriela Surdulescu, Mary E. Travers, Loukia Tsaprouni, Sophia Tsoka, Alicja Wilk, Tsun Po Yang, Krina T. Zondervan

*Corresponding author for this work

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Most genome-wide methylation studies (EWAS) of multifactorial disease traits use targeted arrays or enrichment methodologies preferentially covering CpG-dense regions, to characterize sufficiently large samples. To overcome this limitation, we present here a new customizable, cost-effective approach, methylC-capture sequencing (MCC-Seq), for sequencing functional methylomes, while simultaneously providing genetic variation information. To illustrate MCC-Seq, we use whole-genome bisulfite sequencing on adipose tissue (AT) samples and public databases to design AT-specific panels. We establish its efficiency for high-density interrogation of methylome variability by systematic comparisons with other approaches and demonstrate its applicability by identifying novel methylation variation within enhancers strongly correlated to plasma triglyceride and HDL-cholesterol, including at CD36. Our more comprehensive AT panel assesses tissue methylation and genotypes in parallel at ∼4 and ∼3M sites, respectively. Our study demonstrates that MCC-Seq provides comparable accuracy to alternative approaches but enables more efficient cataloguing of functional and disease-relevant epigenetic and genetic variants for large-scale EWAS.

Original languageEnglish
Article number7211
Number of pages11
JournalNature Communications
Publication statusPublished - 29 May 2015


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